liteindex 0.0.2.dev3

SQLite based queryable python indexes for dicts and lists

LiteIndex

in-process, thread and process safe, easy to use, query-able object storage implenetation for Python

Some use cases, benchmarks and examples:

• cache for a python function
• store and query large number of records without using up memory
• ultra-fast, query-able file storage (faster than creating files on disk and way better for querying)
• exchange data between threads or processes easily
• store application data locally in easy to query and stable format
• Trigger threads/ processes with your custom functions on any specific updates or deletes (combined with gevent or ray, this allows for a extremely easy to use and deploy sqs + lambda like pipeline)
• easily import from csv, tsv, jsonl, any iterable and run queries

pip install --upgrade liteindex

DefinedIndex

• Documentation
• schema has to be specified when creating a new DefinedIndex
• Any python object can be stored in DefinedIndex
• Query language is copy of mongodb queries. All queries are documented.
• Querying can be done on string, number, boolean, json, datetime fields.
• Math can be done on number fields
• blob values can be queried for equality (same file), size.
• other values can only be set, deleted, updated. Cannot be queried upon.
• Insertion order is preserved

from liteindex import DefinedIndex

user_details_schema = {
    "name": "string",
    "age": "number",
    "password": "string",
    "verified": "boolean",
    "address_details": "json",
    "profile_picture": "blob",
    "tag_line": "string",
    "tag_line_embedding": "other",
    "birth_day": "datetime"
}

# db path defaults to memory
index = DefinedIndex(
            name="user_details",
            schema=schema,
            db_path="./test_db.liteindex"
        )

• DefinedIndex Documentation

AnyIndex (In development)

• Like mongodb, any python dict can be inserted at run time
• Doesn't need pre-defined structure unlike DefinedIndex